Due Delivery date: July 31st 2015 Authors: Jean-Luc De Kok VITO E-mail : jean-luc.dekok@vito.be Frede Thingstad UBERGEN E-mail : frede.thingstad@bio.uib.no Tatiana Tsagaraki UBERGEN E-mail : tatiana.tsagaraki@uib.no Robert Thorpe CEFAS E-mail : Robert.thorpe@cefas.co.uk Hugo Salgado UTALCA E-mail: hsalgado@utalca.cl Jennifer Bailey NTNU E-mail: jennifer.bailey@svt.ntnu.no Rachel Tiller NTNU E-mail : rachel.tiller@svt.ntnu.no Russell Richards GRIFFITH E-mail : r.richards@griffith.edu.au

OCEAN CERTAIN FP7-ENV-2013.6.1-1

Project number 603773

Deliverable 4.9 Linking natural science and social-

economics in the DSS WP’s leader: VITO

Principal investigators: Jean-Luc De Kok , VITO (B)

Frede Thingstad, UBERGEN (N) Tatiana Tsagaraki, UBERGEN (N)

Robert Thorpe, CEFAS (UK) Hugo Salgado, UTALCA (CL)

Jennifer Bailey, NTNU(N) Rachel Tiller, NTNU(N)

Russell Richards, GRIFFITH (A)

Project’s coordinator: Yngvar Olsen, NTNU (N)

1. Introduction Deliverable D4.9 reflects on the outcomes of Task 4.3 – Consilience (WP4), Knowledge Integration and Scenario Building. The purpose of this deliverable is to clarify how the outcomes of WP2 (natural sciences) and WP3 (social-economics) are to be combined in the DSS, allowing scenario analysis. The methodological challenges of this task are due to differences in the scientific paradigms used by the natural (WP2) and social (WP3) sciences. Figure 1-1 summarizes the collaborations of and exchanges between the different work packages in OCEAN-CERTAIN.

Figure 1-1 Inter-WP collaboration in OCEAN-CERTAIN (DoW).

Figure 1-1 points to a mutual dependency between the natural and social sciences. On the one hand the natural sciences are to describe the stressors and changes these induce in the system, whereas the social sciences help analyze the social-economic impacts, societal response and adaptive capacity of stakeholders involved. The latter requires a thorough insight in the perceptions and priorities of the actors, which is why two workshop rounds are organized for the three case studies in which sufficient room is given to interactive model building processes allowing exchanges between scientists, local managers and the stakeholders. However, at a certain stage the outcomes of the efforts in WP2, WP3 and WP4 need to be combined and integrated for use in the DSS. The approach to achieve this involves three steps:

WP6

Conceptual Model

Quantitative Model

WP1

WP3

WP2

WP4data mining

text mining

WP5

feedback linkages

quantification

socialresponse

stakeholders

system model

interactive problem structuring with FCMs; scenario analysis with DSS

scenarios

adaptive management strategies

text mining input to FCM design

web-baseddissemination

data delivery

thematicfocus

marine database

BBNs

model improvement

1. translation of the scientific models and data into transparent model concepts at the level of analysis needed for the DSS and stakeholders 2. development of an analytical framework of analysis for the social-economics, and formulation of qualitative scenarios 3. integration of the results of steps 1 and 2 for use in the DSS This deliverable focuses on step 3, however it will be necessary to discuss the progress made in WP2 and WP3 (steps 1 and 2) for proper understanding of the problems to be addressed. This will be done in Section 3 and 4 of this report. The general methodology used for the integration of the natural and social science concepts is Systems Modelling (Forrester, 1961, 1964), more in particular System Dynamics (Forrester, 1961, 1964; Sterman, 2000; Ford, 2010). We will first provide a general introduction of this methodology and the way it is used for natural-social science integration in the DSS which will help clarify how the outcomes of WP2 and WP3 will fit in. A more detailed discussion is provided in Section 5. Systems analysis addresses problems by focusing on the interaction among different components at the level of detail needed. In the context of this project we refer to scientific systems analysis, implying the use of the models and data to support the analysis. A distinction can be made between the conceptual, qualitative phase and a quantitative phase (De Kok and Wind, 2003), but iterations are possible and usually in effect. The qualitative phase focuses on identifying the problem-owners and problems, potential solutions, key variables and processes characterizing the system. This is to result in a conceptual model of the system or system diagram. A rudimentary version of a conceptual model of the system was quickly composed during the project kick-off meeting by asking the project partners to mention the processes and variables they considered to be important for the stakeholders and for the functioning of the Biological Pump. The result was a system diagram that was gradually improved during the first year of the project and that served for communication purposes and as functional design structure for the DSS. The quantitative phase focuses on the collection of data and models needed to quantify the relationships included in the system diagram, resulting in a quantitative model of the system. This model has to undergo testing, calibration to set parameters and initial conditions, and validation by verifying the correctness of the results. System Dynamics Modelling or System Dynamics (SD) originated in the work of Jay Forrester in the late 50s (Forrester 1961, 1964) and has been growing in importance with applications ranging from business analysis (Sterman, 2010) and control problems to environmental modelling and integrated assessment. SD modelling uses the principles of Control Theory (Forrester 1961, 1964) to analyze the impacts of the feedback structure of systems on their time-dependent behavior. Consilience: In OCEAN-CERTAIN Consilience is understood to be the interdisciplinary activity of linking together of data, information and scientific principles from the social and natural sciences, giving it an added value surpassing that of the individual disciplines.

Consilience is a term first minted by the English scientist and philosopher William Whewell in 1840 to denote a “coming together” of different strands of evidence pointing to the same conclusion, and therefore increasing confidence in it. Its relevance for Ocean-Certain is that an integration of approaches from different disciplines in the natural and social sciences can produce a holistic analysis of the behavior of the food web and biological pump, and of their impact on ecosystem goods and services of benefit to society which is more credible and valuable than any single discipline considering the overall problem from its standpoint alone. The practical implementation of the concept of consilience is based on systems analysis (see Section 5).

2. Summary of contribution of involved partners to this deliverable Exchanges at regular intervals of WP5 with the WP2 and WP3 partners to cross-examine the potential and limitations of the knowledge extracted. VITO and UiB are responsible for the coordination of this deliverable, including quality control and editing. NTNU, UTALCA and Griffith University contributed to the introduction and are responsible for Section 4. CEFAS contributed Section 3.

3. Natural Science Model 3.1 The role of Natural Science modelling in Ocean-Certain

The overall purpose of the natural science modelling (WP2) is to develop the natural science understanding necessary to model the impact of multiple stressors (e.g. climate change, overfishing, and pollution) on the overall functioning of the ecosystem food web and biological pump, so that the impact of these at the local, national, and transnational level can be explored, and the knowledge can be incorporated into the decision-making process. This is shown schematically in Figure 3.1.1:

Figure 3.1 : Schematic showing how the modelling framework developed in WP2 (the triangle covering conceptual, hydrodynamic, and trophic energy flow modelling) will integrate the impacts of multiple stressors across the ecosystem to generate outputs that can be used in decision-making and exploring the possible impacts on communities in the three major sectors of fishing, aquaculture, and tourism.

WP2 is doing this through a combination of a) improving our understanding of the way in which the food web and biological pump respond to stressors using i) mesocosm experiments, and ii) conceptual modelling, b) building models which integrate the impact of these stressors across the ecosystem, and c) observing the broad scale state of the natural environment, and the current operation of important ecosystem processes using information from cruises and fieldwork. The manner in which natural science models (WP2) dovetail with social science stakeholder-focused analysis (WP3) and system dynamics model for the DSS (WP5) within Ocean-Certain is shown in Figure 3.1. Natural science modelling is drawing upon insights from WP1 alongside he perspectives of stakeholders to inform model development and set-up. WP3 and WP5 then use the information to explore the possible impacts on local communities and at the EU level, and to aid decision-makers operating at these scales accordingly.

3.2 Nature of the Modelling Framework Modelling of the natural science aspect of the system has four key elements, all buttressed by observations and experimental data. Modelling of the hydrodynamic environment and lower trophic levels is carried out using the European Regional Seas Ecosystem Model (ERSEM, Baretta et al, 1995). ERSEM is an ecosystem model designed to simulate carbon and nutrient cycling and ecosystem response, originally focused on European shelf seas. It is related to NPZD (nutrient-phytoplankton-zooplankton-detritus ) type models but includes several refinements necessary to correctly represent the key processes of temperate shelf ecosystems; the main ones being some plankton community complexity, the microbial loop, variable nutrient stoichiometry, variable carbon: chlorophyll ratios and a comprehensive description of benthic biochemical and ecological processes [Blackford et al., 2004; Ebenhoh et al., 1995, Ruardij et al., 1995]. The units of currency of ERSEM are carbon, nitrogen, phosphorus, silicon & oxygen (see Table 1). ERSEM may be coupled to a range of hydrodynamic models in 1D (GOTM, Allen et al., 2004) or 3D (POLCOMS or NEMO, Allen et al., 2001) which provide information on T&S (temperature and salinity) distributions, mixing and circulation or run alone in ‘aquarium’ mode. ERSEM, or its close relation the Biogeochemical Flux Model (BFM), have been applied to the northeast Atlantic and other systems including tropical upwelling and oligotrophic situations (Blackford et al., 2004) and globally (Vichi et al, 2007) with some success. EwE (Ecopath/Ecosym/Ecospace) based modelling (www.ecopath.org) is the tool of choice to consider the ecosystem impacts on higher trophic levels including fish stocks. EwE has three main components: Ecopath – a static, mass-balanced snapshot of the system; Ecosim – a time dynamic simulation module for policy exploration; and Ecospace – a spatial and temporal dynamic module primarily designed for exploring impact and placement of protected areas. The Ecopath software package can be used to:

• Address ecological questions; • Evaluate ecosystem effects of fishing; • Explore management policy options; • Evaluate impact and placement of marine protected areas; • Evaluate effect of environmental changes.

The foundation of the EwE suite is an Ecopath model (Christensen and Pauly, 1992; Pauly et al., 2000), which creates a static mass-balanced snapshot of the resources in an ecosystem and their interactions, represented by trophically linked biomass ‘pools’. The biomass pools consist of a single species, or species groups representing ecological guilds. Pools may be further split into ontogenetic linked groups called ‘multi-stanzas’: a group may, for example, be split in larvae, juvenile, age 1-2, and spawners (age 3+). Ecopath data requirements are relatively simple, and data is often already available from stock assessment, ecological studies, or literature: biomass

estimates, total mortality estimates, consumption estimates, diet compositions, and fishery catches. The parameterization of an Ecopath model is based on satisfying two ‘master’ equations. The first equation describes the how the production term for each group can be divided:

Production = catch + predation + net migration + biomass accumulation + other mortality

It is the aim with the Ecopath model to describe all mortality factors; hence the ‘other mortality’ should only include generally minor factors as mortality due to old age, diseases, etc. The second ‘master’ equation is based on the principle of conservation of matter within a group:

Consumption = production + respiration + unassimilated food In general, an Ecopath model requires input of three of the following four parameters: biomass, production/biomass ratio (or total mortality), consumption/biomass ratio, and ecotrophic efficiency for each of the functional groups in a model. Here, the ecotrophic efficiency expresses the proportion of the production that is used in the system, (i.e. it incorporates all production terms apart from the ‘other mortality’). If all four basic parameters are available for a group the program can instead estimate either biomass accumulation or net migration. Ecopath sets up a series of linear equations to solve for unknown values establishing mass-balance in the same operation. The approach, its methods, capabilities and pitfalls are described in detail by Pauly et al. (2000). Ecosim provides a dynamic simulation capability at the ecosystem level, with key initial parameters inherited from the base Ecopath model. The key computational aspects are in summary form:

• Use of mass-balance results (from Ecopath) for parameter estimation; • Variable speed splitting enables efficient modeling of the dynamics of both ‘fast’

(phytoplankton) and ‘slow’ groups (whales); • Effects of micro-scale behaviors on macro-scale rates: top-down vs. bottom-up

control incorporated explicitly. • Includes biomass and size structure dynamics for key ecosystem groups, using a

mix of differential and difference equations. As part of this EwE incorporates: o Multi-stanza life stage structure by monthly cohorts, density- and risk-dependent growth; o Adult numbers, biomass, mean size accounting via delay-difference equations; o Stock-recruitment relationship as ‘emergent’ property of competition/predation interactions of juveniles.

Ecosim uses a system of differential equations that express biomass flux rates among pools as a function of time varying biomass and harvest rates, (for equations see Christensen and Pauly, 1997; Walters et al., 2000). Predator prey interactions are moderated by prey behavior to limit exposure to predation, such that biomass flux patterns can show either bottom-up or top down (trophic cascade) control (Walters et al., 2000). By doing repeated simulations Ecosim allows for the fitting of predicted biomasses to time series data. Ecosim can thus incorporate (and indeed benefits from) time series data on:

• relative abundance indices, (e.g., survey data, catch per unit effort [CPUE] data); • absolute abundance estimates; • catches; • fleet effort; • fishing rates; and • total mortality estimates.

For many of the groups to be incorporated in the model the time series data will be available from single species stock assessments. EwE thus builds on the more traditional stock assessment, using much of the information available from these, while integrating to the ecosystem level. Structural stability and robustness of the food web to stressors such as climate change is being addressed through use of the “population dynamic matching model” (PDMM). The PDMM is a generalized food-web model that provides an abstract mathematical representation of the entire marine food-web at species resolution (a parsimonious, but realistic summary of the key drivers of food-web structure and functioning), and is therefore particularly suited to represent the biodiversity effects of varying individual responses to climate change and acidification, and the propagation of these effects to higher trophic levels. The model is currently being set up for looking at questions concerning the stability of the whole ecosystem and changes in its vulnerability to invasive species, which are important questions that cannot be answered by most classes of models which work to a pre-defined and less rich ecosystem structure. The model’s basic parameters need to be set up and varied according to the data provided by WP1 in relation to the typical response ranges of particular species or groups of species to climate change and acidification. An extensive set of computational runs will be performed in order to map these typical variation ranges into PDMM model results, for the future analysis of the larger ecosystem impacts. 3.3: Models for the Case Study Areas Ocean-Certain is considering the ecosystem response to multiple stressors for 3 case study areas, the Barents Sea, Eastern Mediterranean, and Chilean Fjords. Specifications have been defined so as to cover the areas of relevance to key stakeholders whilst ensuring there is enough data to drive the models, suitable higher trophic level models are already available, and the hydrography of the set-up region is physically acceptable. Figure 3.2 shows the set-up regions that have been chosen.

Figure 3.2. Schematic of the case study areas of interest. Yellow circles indicate mesocosm sites, and green stakeholder communities.

A 1-D water column model will drive the biological modelling in each region. Locations for this water column model set-up were finalized during the Ocean-Certain annual meeting in Trondheim (January 2015). These include the Golfo de Ancud for Patagonia (~400-600 m depth, aquaculture interest in the region), southern Barents Sea for the Arctic (~ 200m depth, Atlantic

cod interest) and the Rhodes Gyre for the Mediterranean (~ 2000 m depth, deep water formation interest for carbon storage). The gyre is most known for the formation of Levantine Intermediate water (LIW). Deep water formation events in the center of the gyre during cold winters are also known. However the main feature is the LIW formation. Model set-up has been performed for both the Patagonian site and the Barents Sea, at [42.17 S, 72.73 W] and [73.75 N, 40.00 E] respectively. Meteorological and tidal forcing data has been sourced and applied. Validation data for temperature, salinity and the main nutrients nitrate, phosphate and silica were obtained from international databases and literature. Initial results, focusing on hydrodynamic validation showed the need for inclusion of observational profiles at both sites to relax the model towards the desired hydrodynamic conditions. For the Patagonian site these conditions are permanent saline stratification due to fresh water inputs and for the Arctic site these are the inflow of warm Atlantic waters during winter. As the applied model is a water column model it cannot capture these spatial dynamics unless they are imposed by observational profiles. Our aim is to used results from the ICE-ARC project to facilitate Arctic model set-up within OCEAN-CERTAIN. The model code is currently being developed to incorporate a dynamic iron loop (all sites) and sea ice (Barents Sea). Initial investigation into deep water formation in the model has taken place and resulted in identification of additional capability to be included in the GOTM-ERSEM-BFM model. Initial outlines for combining the lower trophic level modelling work with the mesocosm results have been made and will be followed up in the next stage. Higher trophic level modelling is being carried out with the Ecosim modelling package of the EwE suite which will be linked to the ERSEM biogeochemical model and driven by water column dynamics. The Barents Sea is regarded as a coastal shelf model with a depth of around 200m, the Rhodes Gyre a deep water system with a depth in excess of 350m across its entirety and the Patagonian shelf a transitional region between coastal and deep water. In all cases it is assumed that the pelagic components of the ecosystem dominate the energy flow in the marine environment, with energy moving up the trophic pyramid via the pathway phytoplankton zooplankton small pelagics larger pelagics is the dominant mode of biomass / energy transmission in the marine ecosystem. The Barents Sea Ecosim model used is derived from a model produced by Pinnegar et al. (2005) and the Central Chile model of Neira and Arancibia (2004) is used for the Patagonian case study. There are a lack of suitable Ecosim models for the deep water Eastern Mediterranean so that models such as a north Aegean sea ecopath model (Tsagarakis et al., 2010) will be used as a starting point, benthic and coastal components removed, and information sources such as fishbase used to refine species compositions. The Barents and Chile models have been attached to a dummy lower trophic level using the Couplerlib metadata system. We await delivery of updated lower trophic level system models to complete this process.

3.4 Lower Trophic Level, Conceptual Model

Another component of the Natural Sciences model consists of the minimum model by Thingstad et al. (2007), which simulates interactions in the lower trophic food web using basic principles of ecology. The minimum model has been built to identify the set of main controlling mechanisms, rather than adding numerous elements to obtain a perfect numerical fit between observations and model. Interestingly, the model simulations closely fit observed mesocosm dynamics in several different areas (Thingstad and Cuevas, 2010; Thingstad et al., 2007, 2008). There are three predator populations (heterotrophic flagellates, ciliates and mesozooplankton) performing selective predation on prey allowing coexistence of three groups of osmotrophic competitors (heterotrophic bacteria, autotrophic flagellates, and diatoms) (Figure 3.3). The flow of energy through the food web depends upon relative magnitudes of organism properties such as uptake affinities for nutrients and clearance rates for prey. It is based on the ‘Killing-the-Winner’ principle (Thingstad, 2000), which allows coexistence of inferior competitors with superior competitors due to a stronger top-down control of the competitively superior prey by predators or parasites. The Killing-the-Winner principle is confirmed experimentally on several trophic levels within the pelagic food web ( Haraldsson et al., 2012; Matz and Jürgens, 2003; Steiner, 2003) and is a fundamental element to our conceptual understanding of pelagic ecosystem dynamics.

Figure 3.3. Minimum microbial food web model resolving mesocosm dynamics in different marine environments (from Thingstad et al 2007). The minimum model will be used in conjunction with the results of the mesocosm experiment to ascertain whether the different dominant trophic pathways during experimental manipulations can be explained using this set of basic food web components. Since this model has been shown to be quite robust in such types of experiments the minimum model results will then be compared to dimensionless and one dimensional runs of the ecosystem model (ERSEM) to assess the performance of both models using the same controlled setup.

4. Social-Economic Model

4.1 Introduction The overall objective of WP 3 is to improve understanding of the impacts on human communities of changes to marine systems resulting from multiple stressors. As noted elsewhere in this report, these stressors include 1) changes in macro and micro nutrient supply, 2) changes in temperature, 3) acidification, 4) changes in light, 5) deoxygenation, 6) pollution, and 7) overfishing. The effects of climate change interact with these stressors in complex ways. Given the number and complexity of these stressors, establishing impacts on human communities is extremely difficult. Ocean Certain, however, provides an avenue for understanding the impact of these complex changes by focusing on their combined impact of the marine food web and on the biological pump (that removes CO2 from the marine system). Work package 3 in turn links changes in the food web to human communities. Linking changes in the food web (and other aspects of marine systems) to human communities is complex. OC focuses on the lower trophic levels of the food web: changes at this level may have a direct impact on the resources upon which human communities rely, but they are also likely to have many indirect impacts on these communities. Establishing these linkages are among the more challenging tasks for the project and the work package. WP 3 addresses one part of this problem by establishing which marine resources are of most direct concern to specified human communities, which will allow other WPs (especially WP 2) to work on establishing critical linkages between the lower trophic levels of the food change to resources upon which human communities depend. Understanding the impact of changes in marine systems on human communities adds an additional layer of complexity. As a first step in approaching this problem, WP 3 has selected three geographic areas for study: Tromsø, Norway; Çeşme, Turkey; and the Relvoncaví Fjord in Chile. For each case study areas, three economic sectors that will be most immediately and most directly impacted by changes in marine systems are being studied. The selected economic sectors are fisheries, aquaculture and tourism. Understanding the impact of changes in marine systems on these three economic sectors is addressed conceptually using the Vulnerability Analysis for Sustainability (VAS) as developed by Turner II et al. (2003) (described below). In this approach, impact is understood to be a function of the exposure of the place (and, here, sector) to a given change, the sensitivity of the human and natural systems to the change, and the resilience of both systems under stress. The adaptive capacity of the local community is a key factor with respect to their resilience. The major work of WP 3 is an assessment of the sensitivity and resilience of human communities to changes in marine systems that impact the three key economic sectors. This work is being done by first, developing an overview of the socio-economic situation of the communities and their national context (Deliverable D 3.3). Second, and more importantly, WP 3 is holding a series of stakeholder workshops in each of the three coastal communities. A separate workshop will be held for each economic sector in each community (to the extent this is possible). A second round of workshops will be held in order to check and refine the findings of the first set, to allow for the interface with decision support system (DSS) development by WP 5, and to develop the game theory application under development in WP3 (Deliverable 3.11).

Stakeholder workshops are particularly well-suited to tackling the complexities of understanding the vulnerability of local communities to changes in their marine systems, as well as for capturing the information necessary for developing good management strategies and practical, user-friendly DDSs. Stakeholders are “experts” in their own professions, resources and geographic areas and perform tasks of adaption to changing conditions of all kinds on a daily basis. As the economically active agents “on the ground” they make such decisions on a daily basis. In so doing, they weigh and make choices with respect to a variety of factors – economic, social and environmental/resource related. The workshops are designed to draw out the stakeholders’ understandings of how their socio-ecological system works, what factors they judge to be the most important determinants of their ability to carry on their economic activities and how sensitive they are to changes. Insights into the way stakeholders see their world provides critical information for improving management of the resources these economic sectors are dependent upon, as well as for improving management of the sectors themselves. The workshops will provide information about what factors are most critical to stakeholders and which are thus the primary determinants on their decision-making. These may be very different from the factors that scientists and distance policy-makers think should be important. By understanding what matters most to stakeholders, DSSs can be designed to include key variables of concern to stakeholders, presented in such a way that these are easy for stakeholders to grasp. In addition, by understanding the key determinants of stakeholder decision-making, managers may be able to design measures and initiatives that more effectively achieve policy goals. Finally, understanding the key determinants of stakeholders’ decisions, managers and policy makers may be better able to anticipate the ways in which communities adapt to change, and to enhance or discourage these as needed. Finally, human activities feed back into natural systems: understanding how communities are likely to respond to change can help anticipate whether human adaptation will strengthen or weaken the forces that are driving changes in ocean systems. It is in connection with this aspect of the project that the game theoretical application to the existing workshop methodologies will be developed. One factor that will impact community resilience (including adaptive capacity) is whether the community is highly cohesive (that is, whether it has a high degree of social capital) or whether it is divided (conflicted). The stakeholder workshops will provide an insight into this aspect of resilience in two ways. First, the efforts to set up the workshops, which include getting participants to agree to attend these, can be very revealing of the general situation in the community. Second, in the process of discussing the way the socio-economic system works, stakeholders usually explain their views, often at length. These remarks create a “narrative” to accompany the model building, and often provide important insights into community cohesion and division. Finally, WP3 will investigate local newspaper databases to establish the state of the community. This information too can be useful input into management decisions. WP 3 will also examine the degree and nature of “climate change skepticism” in the three countries and in the three communities, using media mining, polling information, and the narratives produced in the workshops, if applicable. The general model will focus on the socio-economic vulnerability of coastal communities, especially on the three target sectors we identified as most directly impacted by changes in the food web for OCEAN-CERTAIN. Socio-economic vulnerability is recognized as a function of

the sensitivity of the sector to the climatic and non-climatic stressors, the degree to which the sector will be exposed to these stressors (exposure) and the ability of the natural and socio-economic systems to adapt to changes (IPCC 2007). The analysis will focus on the vulnerability of the fishing, aquaculture and tourism sectors, with particular attention paid to the degree to which trade-offs might occur among these sectors, but will also on key areas of broader community vulnerability: socio-economic development, gender vulnerability and general health and welfare. The analysis will also take timeframe into account: both the pace of change and how far into the future stakeholders can plan are important. In the case studies, it will therefore be possible to identify differences in the socio-economic and institutional characteristics of the three countries under analysis, which will later be crucial to the understanding of different aspects of the general model. 4.2 Vulnerability Model The basis of the vulnerability model used in OCEAN-CERTAIN is the Vulnerability Analyses for Sustainability (VAS) model of Turner et al (2003). Adaptation of their VAS model provides a focus for developing case study vulnerability models to explore the potential impacts of climate change on social-ecological systems. Context for the VAS is given by Turner et al.’s definition of vulnerability that frames their model: “the degree to which a system, subsystem, or system component is likely to experience harm due to exposure to a hazard, either a perturbation or stress/stressor”. This definition builds on climate-related themes of exposure (nature and degree to which a system experiences environmental or socio-political stress), sensitivity (the degree to which a system is modified or affected by perturbation) and resilience (the ability of a system to “handle” or “bounce back” from perturbations, retaining its defining structures and functions). Furthermore, it explicitly acknowledges and incorporates the need for a “coupled systems” approach. Turner et al.’s approach has additional characteristics that make it particularly suited for the OCEAN-CERTAIN project. It is a conceptual framework that is well-suited to a case study approach, appreciative as it is of the range of (potentially interacting) factors that can affect the primary dimensions of vulnerability (exposure, sensitivity and resilience). Turner et al. emphasize the importance of “multiple interacting perturbations and stressors/stresses and the sequencing of them”. The framework is sensitive to place but also locates any given place in its larger context of “nested scales and scalar dynamics of hazards, coupled systems and their responses” (Turner et al., 2013:8075). Turner et al. also explicitly include institutional structures in their conceptualization of adaptive capacity (2013:8075) along with feedbacks to both environmental and human conditions. The latter is an important ‘system’ characteristic that helps explain observed system behaviors such as the tragedy of the commons or limits to growth (Ford, 2010). In OCEAN-CERTAIN, we build upon the framework developed in Turner et al. (2003) to adapt it to the impacts of climate change on the food web and the biological pump and the subsequent impacts on environmental services that are important for a given community, as well as to the feedbacks created by these communities to the natural system when dealing with the adaptation and mitigation of the effects of climate change.

The VAS model framework The process for developing a generalized VAS model for OCEAN-CERTAIN is summarized in Figure 4.1. This is characterized by two key components: (1) development of an Environmental Model that shows the environmental responses to climate change; and (2) Socioeconomic Vulnerability Model that maps out the socio-economic side based on four concepts important to understanding socioeconomic vulnerability of a community to climate change (Environmental exposure; Socioeconomic sensitivity; Socioeconomic Entitlements; Public Policy and Private Responses).

Figure 4.1: General Structure of the environmental and socioeconomic models of vulnerability. Development of the Environmental Model component of the VAS model The first step to begin the application of the adapted VAS model to the three cases of interest in the OCEAN-CERTAIN project is to create a clear and concise conceptual model of the different effects that climate change will have on the biological pump and the marine food web and how this will interact with the coastal communities. To understand this complex process, we performed a workshop with all the scientists involved in the OCEAN-CERTAIN consortium during the Kick-off meeting in Amsterdam on November 25th-27th, 2013. During this workshop a shared mental map (Ford, 2010) of the different concepts and their relationships was built. Later on, this map was refined and simplified using a Fuzzy Cognitive Mapping technique.

The system thinking method consists of an open discussion among the involved participants whereby they (the participants) identify the system boundaries, key variables, relationships

Socioeconomic Vulnerability Model

Environmental Model

Shows how climate change will affect biological pump, the marine food web and how this will affect the local ecosystem. Climate Change will affect:

Oxygen, nutrients, zooplankton, phytoplankton, etc.

Socioeconomic sensitivity

Sensitivity of key resources and ecosystem services affected by Climate Change.

Socioeconomic entitlements

Resources available to communities to cope with natural ecosystem variations.

Public Policy Responses Available policy that can be taken by the

government and public agencies (public adaptation policies)

Stakeholders Responses How stakeholders respond to ecosystem

changes and policies (private adaptation strategies)

Feedbacks from socioeconomic model

Effects of ecosystem changes on local communities

(interactions or arcs) and priorities of the group contextualized around a focus question and a set of system drivers pre-selected by the research group. This process helps to introduce (or reinforce) the systems-context of the problem to the participants, provides a forum for consensus building and provides a mechanism for integrating ‘partial knowledge’ of the participants about the system into a single model. Overall, this discussion took approximately 30 minutes (but can take several hours). The practical steps of the systems thinking process included: • The facilitator introduced the participants to the language of systems thinking and the

objectives of the session • The facilitator prompting the conceptual-model building process by presenting a suite of pre-

selected external “drivers” (factors that may impact the system without being impacted by the system).

• The facilitator using a white board to post the elements that the participants identified as important determinants of whether (and how) climate change will affect the marine environment and coastal communities.

• The participants identified which of the elements that they had identified were causally linked (cause-effect). Directed arcs were then drawn between the identified elements (on the white board) to represent the direction of causality.

This process produced the systems conceptual diagram shown in Figure 4.2. Important features of this conceptual diagram were the seven drivers (shown as teal colored boxes in Figure 4.2): 1) Reduction in light; 2) Changes in Micro and Macro Nutrients; 3) Increases in Temperature; 4) Acidification; 5) Deoxygenation; 6) Pollution; and 7) Overfishing. The workshop yielded a group-level consensus mental model of how the socioecological system (bounded within the research of OCEAN-CERTAIN) is interconnected based on the knowledge and perceptions of the workshop participants. Direct translation of the conceptual model to a process-based numerical model (e.g. stock and flow model) is difficult because of the high levels of complexity and parameter uncertainty (aleatoric and epistemic). However, these conceptual models can provide insight into system structure, and more importantly, the nature of feedback pathways (balancing and reinforcing) that help to explain the dynamics (behavior over time) of a ‘system’. System structure and system behavior are important tenets of ‘systems thinking’ and ‘system dynamics’ (Forrester, 1961, 1964; Sterman, 2000; Ford, 2010) and are used to explain system behaviors (e.g. success to the successful; limits to growth) that can include unintended consequences (e.g. tragedy of the commons; fixes that fail). Furthermore, understanding the characteristics of the system (and by extension, system archetypes), intervention points can be identified for changed system behavior. Diagnostics carried out on the systems conceptualization (Figure 4.2) included causes and effects pathway assessments (Figures 4.3-4.4). This process enables insight into the elements (and patterns of elements) that affect (causes) or are affected by (effects) some element of interest. For

example, in the causes example (Figure 4.3), the element of interest is ‘Biological Pump’ and the first- (directly connected) and second- (connected via an intermediate element) generation determinants (of the biological pump) are shown. In the effects example (Figure 4.4), the element of interest is ‘Increases in Temperature’ and the first- (directly connected) and second- (connected via an intermediate element) generation of child elements are shown.

Figure 4.2 Shared Conceptual Map from the System Thinking Exercise

Figure 4.3 : A ‘causes’ tree for the element ‘Biological Pump’ using the conceptual model developed from the System Thinking Exercise. Elements shown in parentheses (e.g. ‘(bacteria likes iron)’) indicate multiple pathways for this element acting on ‘biological pump’.

LTL model result

Model driver

HTL model result

Linking LTL to HTL

biological pump

change ecosystem

affect lower food web

bacteria likes iron

favor species high turnover and more sensitive to climate fluctuations

larvae

Overfishing

harder to make calcium carbonateAcidification

decrease in ph

more jellyfishless fish

Reduction in Light

smaller particles, less energy transfer to upper troffic level(bacteria likes iron)

(change ecosystem)

Figure 4.4: An ‘effects’ tree for the element ‘Increases in Temperature’ using the conceptual model developed from the System Thinking Exercise. Elements shown in parentheses (e.g.

‘(corals)’) indicate multiple pathways that this element is affected by ‘biological pump’. Development of the Socioeconomic Model component of the VAS model Overall, the conceptual model generated by the OCEAN-CERTAIN scientists during the system thinking exercise (Figure 4.2) provides a process for identifying the pathways that climate change will impact on environmental variables that are important for the provision of environmental services to the fisheries, aquaculture and tourism activities (i.e. the three case studies). This conceptual model is the first piece of our general model of socioeconomic vulnerability that will facilitate the development of Turner et al.’s VAS model for OCEAN-CERTAIN. The second important component of our model lies with the development of the socio-economic model that is a major component of the overall model structure (see Figure 4.5). In this aspect four concepts are considered to be important for understanding socioeconomic vulnerability: (1) Environmental exposure: The extent that local environmental services are affected by climate

change. The characteristics and attributes of this exposure will be dependent on, and influenced by, local conditions and therefore expected to vary across the study sites.

(2) Socioeconomic sensitivity: How local communities (specifically the fisheries, aquaculture and tourism sectors) are/will be affected by climate change and how this manifests via changes in environmental services.

(3) Socioeconomic Entitlements: Resources and institutions available to stakeholders for coping with the effects of climate change on the environmental services.

(4) Public Policy and Private Responses: The specific ways of action that can be taken at a private (individual) or public (collective) level to deal with the effects of climate change.

Increases in Temperature

Changes in Micro and Macro Nutrientseutrophication

toxic algae bloom

corals

benthic animal die

fish die

unpleasent for tourist

Deoxygenation

(Changes in Micro and Macro Nutrients)

dead zone

hidrodensulfite?

hydrological cylce(more water from ice)

(rain wash soil from land)

microbial pathogens

(corals)

diseases

seafoood

shell fish

more water from ice(rain wash soil from land)

Reduction in Light

rain wash soil from land (Reduction in Light)

A specific representation of the general model of socioeconomic vulnerability is presented in Figure 4.5. Eight variables were identified during the kick-off meeting that are important within the natural ecosystem and that will potentially be affected by climate change: 1. The food web, 2. The biological pump function, 3. Sea surface temperature (SST), 4. The concentration of CO2 in the ocean, 5. Ocean acidification (relates to point 4), 6. Water quality, 7. Water pollution and 8. Algal blooms. These variables will generate impacts on key resources (e.g. fish, shellfish in Figure 4.5) that will be responsible for the “exposure” part of the vulnerability model. The “sensitivity” of different communities to changes in these resources is the next level of analysis in our adapted VAS model. Here, different stakeholders, such as artisanal fishers, industrial fishers, aquaculture companies, processing plants, tourism industry and the general community could feel themselves affected in different ways by the changes in the quantity or quality of the environmental services that are important in each location. Once they feel an effect they face different entitlements, resources and public institutions that could allow the local stakeholders to take actions to deal with the short and long run effects of climate change on their communities. The different actions that they can eventually take and the capacity they have to actually take actions that allow the community to cope with the effects of climate change will determine their private and public adaptive capacity. All these concepts together will define the vulnerability of a given community to the effects of climate change. A community will be vulnerable to climate change when the natural marine ecosystem be affected by the different stressors involved in the climate change process, when these changes create impacts over goods and services that are important for stakeholders, when these stakeholders are sensitive to the changes experimented in these resources and when they have limited responses, public and private, to these effects. Different degrees of effects in these different levels will create different vulnerability of the communities under analysis, both at a national and at a local level.

Figure 4.5: The OCEAN-CERTAIN general model of socioeconomic vulnerability.

5. Natural-social science integration in the DSS

Decision support systems in the domain of environmental management have been developed from the early 90s onwards, with varying degrees of acceptance among stakeholders, end users and scientists. The purpose of a DSS is to translate scientific knowledge and data into a format that is understandable for environmental managers and, often, the general public, thereby bridging the gap between science and policy. The challenge lies in balancing the complexity and robustness of the scientific models used and the accessibility and utility of the DSS for decision makers. An operational definition of Consilience was given in the introductory Section. The main goal is to bring together the knowledge and expertise of natural and social scientists in a transdisciplinary collaboration. This collaboration is to give an added value transcending the potential of the individual disciplines. The concept of consilience is closely linked to interdisciplinarity. Although interdisciplinary science is growing in importance the practice of interdisciplinarity is not straightforward. The typical problems experienced in projects and identified in the literature include:

- differences in scientific paradigm between the natural and social sciences - a difference in focus on modelling vs stakeholder-oriented research (‘hard’ vs ‘soft’

systems approaches (Pahl-Wostl, 2007) - differences in the way uncertainties are perceived and dealt with or accepted

A typology of multi-disciplinarity with different modes of collaboration was given by Huutoniemi et al. (2010) in (Huutoniemi et al., 2010; Haapaasari et al., 2012). Cross-disciplinary integration can be differentiated in three categories with an increasing degree of integration (Haapasaari et al., 2012; Beichler et al., 2014): multi-, inter- and transdisciplinary science. In multi-disciplinary projects the researchers work on a common problem, but the collaboration is characterized by a loose linkage of subprojects (Haapaasari et al., 2012). In inter-disciplinary projects the collaboration is built on interactions between different disciplines, with the exchange of data, methods and data, and researchers crossing disciplinary lines (Beichler et al., 2014). The strongest form of interdisciplinarity is found in transdisciplinary projects (Barry et al., 2008; Mobjörk, 2010; Stock and Burton, 2011; Beichler et al., 2014). Here new scientific concepts are developed from the cross-disciplinary science. Conceptual frameworks cannot directly be used to integrate natural and social science knowledge in a decision-support system. Beichler et al. (Beichler et al., 2014) use socio-ecological resilience as a bridging concept for interdisciplinarity. They point out that the reason to use a bridging concept is that the social and ecological system cannot be analyzed separately, due to the presence of feedback and interactions. System Dynamics models or SD models (Forrester, 1961; Forrester, 1964; Forrester 1969; Sterman, 2000) provide a natural platform for interdisciplinary integration, obliging researchers to contribute to a common framework of analysis. The added value of SD models lies in the explicit consideration for system feedback, which can result in counterintuitive response to policy choices, particular in the long-term. The application of SD models, however, does not automatically solve the problems of paradigmatic and language differences. Systems Dynamics Modelling is inherently dependent on the quantification of the processes studied, thereby favoring the subjects studied by the natural sciences. The question arising is how the DSS can integrate the natural and social-economic subsystem models without compromising the strengths

and particularities of the individual disciplines too much. For OCEAN-CERTAIN, the decision was made with WP2 to use meta-modelling and equilibrium solutions for the natural subsystem. For the social subsystem, the knowledge development in WP3 focuses on the stakeholder perceptions and their response to changes (vulnerability, resilience and adaptive capacity). The integration of social science knowledge in the DSS is more challenging. Two techniques are being applied in parallel in OCEAN-CERTAIN to extract information from key stakeholders and support the communication between researchers, stakeholders and environmental managers (see deliverables D3.2, D3.3 and D5.1). Fuzzy Cognitive Maps or FCMs (Kosko, 1986; De Kok et al., 2000; Kok, 2009; De Kok et al., 2014; Gray et al., 2015) are directed, causal graphs which can be used to describe the dynamic feedback behavior of systems of varying complexity and help bridge the gap between qualitative and quantitative knowledge. Their application is prognostic (“what if?”). Bayesian Belief Networks (BBNs) (Jensen, 2001; Boulanger and Bréchet, 2005) – use a probabilistic approach to explore acyclic causal structures and diagnose the chance of an event occurring by making inferences about the conditional dependencies between variables (“why?”). The underlying probabilistic framework of Bayes’ Theorem enables integration of interdisciplinary variables in a single model structure. Furthermore, data requirements (for model parameterization) are minimized because probabilities for any given ‘child’ variable in the model are only conditional on the values of its ‘parent’ variables. In summary, FCMs are prognostic, deterministic and cyclic, whereas BBNs can be both used for diagnostic and prognostic purposes, are probabilistic-based (thus integrative) but are restricted to acyclic model structures. Both techniques have systems thinking, the hierarchical structuring of knowledge in nodes, and support for participatory knowledge extraction in common. The Decision-Support System (DSS) developed in OCEAN-CERTAIN is intended to serve three purposes. First, it should be a policy tool which can support the communication between decision makers and stakeholders by analyzing and comparing the impacts of different management options on selected key indicators (answering ‘what-if’ questions). For example, what might be the impact of climate change on the sustainable level of fishing pressure? Second, it provides a flexible platform for the integration of models from different disciplines (Consilience) - the engine of the DSS is a system model. The models for the lower and higher trophic level will not be integrated directly but in the form of metamodels linking selected model input and output (for example, a matrix linking fishing pressure to biomass is a metamodel). Updates of data and model concepts should be manageable. Third, the DSS should allow visualization of (uncertain) human behavior and adaptation to changes, making the narrative storylines of WP3 more explicit. A web-based DSS mock-up was presented during the Trondheim project meeting in January 2014 (Figure 5.1). The mock-up guides potential users through a series of questions to get a better understanding of the functional requirements for the DSS. The mock-up was presented to stakeholders or potential end-users during the WOC Business Forum (New York, September 2014) but not yet to stakeholders for the case studies due to a delay in the planning of the WP3 workshops.

Figure 5.1 Mock-up for the OCEAN-CERTAIN DSS. For OCEAN-CERTAIN the following aspects are considered essential for the DSS design:

• stakeholder priorities resulting from the workshops organized • lower trophic level food web model • higher trophic level model • stressors driving change • ecological and physical indicators for the system state • social-economic indicators (vulnerability, resilience being the two most important ones) • adaptive behavior of actors (fishermen, tourist operators, tourists, …)

Flexibility, transparency, validity and user-friendliness are the most important design requirements. In addition the DSS should allow comparison of scenarios for the three study areas. During the third progress meeting (Trondheim, January 2014) the decision was taken to use a non-spatial model for the higher trophic level and a 1D model for the lower trophic level (food web). Different software platforms are available for designing system dynamics models (e.g. Stella, VenSim, ExtendSim). Common features are the graphical interface for constructing and analyzing models, the short runtimes and lack of support for explicit or gridded spatial modelling. ExtendSim (www.extendsim.com) was chosen for the implementation of the DSS because of the positive experience with this platform in the EU-FP6 Research Project SPICOSA (www.spicosa.eu), the flexibility of the ModL programming language with a strong similarity to C, the possibility to organize data in databases, and the organization of reusable model constructs in

libraries (De Kok et al., 2015). The graphical tools available in ExtendSim are convenient for customizing the user interface with control options and visualization of scenarios. The system model underlying the DSS model core should link the stressors (e.g. change in SST) to the impacts (e.g. change in jellyfish abundance) and the resulting exposure (e.g. the effect of changed jellyfish abundance on tourism). Then, depending on the sensitivity for this exposure, the stakeholders may have a range of adaptation options available for minimizing the negative effects and/or harnessing new opportunities that arise (adaptation). The degree of adaptation that occurs (adaptive capacity) and the level of exposure combine to determine the vulnerability of the ‘stakeholders’ to the impact. Conceptually, a systems framework is needed to analyze these steps in sequence in a transparent manner (Figure 5.2). The framework should be flexible enough to deal with changes or updates in the model concepts and allow for feedback as mentioned in Section 3

Figure 5.2 Natural-social science integration in the DSS system framework with examples. The magnitude of vulnerability of stakeholders to an impact is the combined result of exposure to changes (for example a loss in tourism income), their capacity to adapt (for example changing to other economic activities which are less dependent on the ocean ecosystem), and the ecosystem services provided (recreational value etc.). The need and feasibility to include the feedback from the social subsystem to the ecosystem (response) is still being discussed.

Qu

Qu

STRESSOR(SST increase)

IMPACT(Jellyfish appearance)

EXPOSURE(Tourism Loss)

ADAPTIVE CAPACITY (alternative income)

VULNERABILITY

ECOSYSTEM(change)

natural science domain (WP2)

social-economic domain (WP3)

ecosystemservices

consilience (WP4)

response

metamodelling

scenarios

Bayesian Belief Networks (BBNs) built through participatory workshops can be used to quantify the perceptions of stakeholders on their vulnerability and adaptive capacity (Richards et al., 2013). Furthermore, these perceptions can be directly integrated with physical variables from the natural science domain (consilience). The participatory nature of BBN development provides transparency into the model building process, enhancing trust from the stakeholders in the model outcomes. It also promotes communication between stakeholders (i.e. within a workshop environment), helping to build consensus about the condition of the ‘system’. Techniques such as sensitivity analysis, times surprised and confusion matrices (where data is available for the latter two) can then be used to explore dominant pathways in the BBN. The narratives that emerge during the participatory model building process also provide important contextual information (Richards et al., 2013). Finally, the probabilistic basis of BBNs (Bayes theorem) means that uncertainty at any particular node in the BBN is propagated throughout the model itself in a mathematically robust manner (this includes updating the model when uncertainty is reduced through ‘hard evidence’) – this is a salient issue when dealing with systems (socio-ecological) and conditions (climate change) where there is strong and/or changing uncertainty. The acyclic restriction of Bayesian networks (see earlier text in this section) means that the BBNs are developed in the absence of feedback pathways. This restriction is addressed by combining the development of BBNs with Systems Thinking during the stakeholder workshops where the BBN(s) are developed. The Systems Thinking process acts as a primer for developing the BBN by causing the workshop participants to think of their ‘problem’ in the context of a system where structure and feedbacks are important. For example, a combination of Systems Thinking and BBNs was used in the Norwegian-Chilean CINTERA project (Tiller et al., 2014) where the ‘problem’ being addressed was “how will your options change with a ten-fold increase in aquaculture?” By first developing a systems conceptualization using Systems Thinking, the ‘options’ explored by the stakeholders (in the CINTERA project) using a BBN were aligned to the system that was being operated within (e.g. where the stakeholders were fishers, their ‘system’ included the social, economic and environmental variables and interconnections that they believed comprised the fishing sector). That is, the Systems Thinking process provided a mechanism where consensus about the structure of the ‘system’ emerged from the stakeholders. From this shared view of the system, the dominant issues (often signified by the variable with the most connections) could be then discussed and used to identify a starting point for constructing a BBN around. Fuzzy Cognitive Maps can help trace the temporal sequence of events to gain a better understanding of the interdependencies between stressors driving the system and management indicators. An application of a fuzzy cognitive map for the conflict between fish farming and catch fisheries (Tiller et al., 2013) was presented during the IMBER 2014 Open Science Meeting held in Bergen (De Kok et al., 2014). The fuzzy cognitive map was used to analyze and compare different scenarios qualitatively (Figure 5.3).

Figure 5.3 Comparison of scenarios for impact of fishing pressure on vulnerability using fuzzy cognitive mapping presented at IMBER 2014 (De Kok et al., 2014). The design issue to address is how the narratives, and stakeholder knowledge captured in BBNs and FCMs can be included in the DSS and integrated with the natural science submodel, giving the systems model an added value surpassing that of the two subsystems (Figure 5.2). Often, collaborations of natural and social scientists aimed at developing a DSS as a common deliverable do not fulfill this objective, i.e. the projects are multi- rather than inter-disciplinary (see Section 5.1). To avoid this common problem, the DSS architecture in OCEAN-CERTAIN should allow for the visualization and storage of 1. the contextual narrative storylines, 2. the BBN vulnerability models developed in the workshops with the stakeholders, and 3. the feedback structure captured in the FCMs. The proposal is to refine the systems framework of Figure 5.2 in the following way to achieve this:

1. The Bayesian Belief Networks and Fuzzy Cognitive Maps will be included as diagrams and made available.

2. The contextual narrative storylines will be made available and linked to the scenario options

3. The analyses with the BBNs will be translated into Conditional Probability Tables (CPT) and used in the system model to include the behavioral aspects (political will, adaptative behavior, … ). For an example implemented in ExtendSim see https://www.linkedin.com/pulse/using-bayes-theorem-simulation-gregory-hansen

4. FCMs will be included as matrix and allow for analyzing the impact of system feedback on the vulnerability and other social indicators, showing the dynamic sequence of events in support of the narratives that have been constructed

5. Most importantly, the metamodel for the natural subsystem will be allowed to interact with the BBN e and FCM models.

6. Updates of the natural subsystem metamodel, BBN matrix and FCM matrix, both in terms of the number and type variables, and the quantitative dependencies should not be too difficult.

sustainedfishing effort

decreasingfishing effort

These design requirements should ensure a flexible and transparent platform for analyzing the potential impacts of climate change on key stakeholders and their capacity to deal with the impacts expected. The ability to combine qualitative and quantitative knowledge gives the DSS an added value, strenghtening the interaction between stakeholders, social and natural scientists.

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Climate Change OverfishingPollution

TemperatureSalinityIce Melt

pHO2

LightNutrients

Direct Mortality

Natural Science Models

ConceptualLTLHTL

Stock biomassesFood web function

and robustnessProductivity

Nutrient levels

Carbon fluxes at depth

CO2 export and impact on ocean

CO2 uptake

WP3Social Science

WP5Decision Support

Fisheriers Aqua-culture Tourism Decision

Makers

WP 2 Inputs to WP5:Emulator expresses these

in terms of driving stressors for wide range of

scenarios

WP3 inputs shaped by stakeholder requirements

Figure 3.1.2 Schematic of the project linkages on the path from stressors to stakeholders, illustrating the linkages between WP2, WP3, and WP5 that are facilitated through WP4.

ERSEM State Variables State variable

elements description

Pelagic N1 p phosphate N3 n nitrate N4 n ammonium N5 s silicate P1 cnps diatoms P2 cnp flagellates P3 cnp picoplankton P4 cnp dinoflagellates P5 cnp Coccolithophores (not yet in 3D) B1 cnp bacteria Z6 cnp heterotrophic nanoflagellates Z5 cnp microzooplankton Z4 cnp mesozooplankton R1 cnp dissolved labile organics R2 cnp refractory labile organics R4 cnp small particulate organics R6 cnps medium particulate organics R8 cnps large particulate organics O2 o oxygen O3 c dissolved inorganic carbon Benthic K1 p phosphate K3 n nitrate K4 n ammonium K5 s silicate H1 cnp aerobic bacteria H2 cnp anaerobic bacteria Y2 cnp deposit feeders Y3 cnp suspension feeder Y4 cnp meiobenthos G2 o oxygen G3 c dissolved inorganic carbon D1 m oxygen penetration depth - metres D2 m nitrate / sulphide horizon Q1 cnp dissolved organic matter Q6 cnps particulate organic matter Q7 cnps buried organic matter Derived Carbonate System Parameters TA Alkalinity

pH pH pCO2w Partial pressure HCO3 Bicarbonate CO3 Carbonate Oma Aragonite saturation state Omc Calcite saturation state Table 1: ERSEM model state variables.